SoroushVahidi/scidocs huggingface dataset:
Centralities in Large Networks: Algorithms and Observations
(2011)

Node centrality measures are important in a large number of graph applications, from search and ranking to social and biological network analysis. In this paper we study node centrality for very large graphs, up to billions of nodes and edges. Various definitions for centrality have been proposed, ranging from very simple (e.g., node degree) to more elaborate. However, measuring centrality in billion-scale graphs poses several challenges. Many of the “traditional” definitions such as closeness and betweenness were not designed with scalability in mind. Therefore, it is very difficult, if not impossible, to compute them both accurately and efficiently. In this paper, we propose centrality measures suitable for very large graphs, as well as scalable methods to effectively compute them. More specifically, we propose effective closeness and LINERANK which are designed for billion-scale graphs. We also develop algorithms to compute the proposed centrality measures in MAPREDUCE, a modern paradigm for large-scale, distributed data processing. We present extensive experimental results on both synthetic and real datasets, which demonstrate the scalability of our approach to very large graphs, as well as interesting findings and anomalies.
Phonetic posteriorgrams for many-to-one voice conversion without parallel data training
(2016)

This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.
Telos: Representing Knowledge About Information Systems
(1990)

We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies).
We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.
Fighting Accounting Fraud Through Forensic Data Analytics
(2018)

Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.
Mindfulness and emotion regulation in depression and anxiety: common and distinct mechanisms of action.
(2013)

BACKGROUND
The current study seeks to investigate the mechanisms through which mindfulness is related to mental health in a clinical sample of adults by examining (1) whether specific cognitive emotion regulation strategies (rumination, reappraisal, worry, and nonacceptance) mediate associations between mindfulness and depression and anxiety, respectively, and (2) whether these emotion regulation strategies operate uniquely or transdiagnostically in relation to depression and anxiety.


METHODS
Participants were 187 adults seeking treatment at a mood and anxiety disorders clinic in Connecticut. Participants completed a battery of self-report measures that included assessments of depression and anxiety (Mood and Anxiety Symptom Questionnaire), and emotion regulation (Ruminative Response Scale, Penn State Worry Questionnaire, Emotion Regulation Questionnaire, Difficulties in Emotion Regulation Scale).


RESULTS
Simple mediation analyses indicated that rumination and worry significantly mediated associations between mindfulness and anxiety symptoms, whereas rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms. Multiple mediation analyses showed that worry significantly mediated associations between mindfulness and anxiety symptoms and rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms.


CONCLUSIONS
Findings suggest that mindfulness operates through distinct and common mechanisms depending on clinical context.
The principles and practice of probabilistic programming
(2013)

Probabilities describe degrees of belief, and probabilistic inference describes rational reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded onto the scene of modern artificial intelligence, cognitive science, and applied statistics: these are all sciences of inference under uncertainty. But as probabilistic models have become more sophisticated, the tools to formally describe them and to perform probabilistic inference have wrestled with new complexity. Just as programming beyond the simplest algorithms requires tools for abstraction and composition, complex probabilistic modeling requires new progress in model representation—probabilistic programming languages. These languages provide compositional means for describing complex probability distributions; implementations of these languages provide generic inference engines: tools for performing efficient probabilistic inference over an arbitrary program. In their simplest form, probabilistic programming languages extend a well-specified deterministic programming language with primitive constructs for random choice. This is a relatively old idea, with foundational work by Giry, Kozen, Jones, Moggi, SahebDjahromi, Plotkin, and others [see e.g. 7]. Yet it has seen a resurgence thanks to new tools for probabilistic inference and new complexity of probabilistic modeling applications. There are a number of recent probabilistic programming languages [e.g. 8, 9, 11–17], embodying different tradeoffs in expressivity, efficiency, and perspicuity. We will focus on the probabilistic programming language Church [6] for simplicity, but the design of probabilistic languages to best support complex model representation and efficient inference is an active and important topic. Church extends (the purely functional subset of) Scheme with elementary random primitives, such as flip (a bernoulli), multinomial, and gaussian. In addition, Church includes language constructs that simplify modeling. For instance, mem, a higher-order procedure that memoizes its input function, is useful for describing persistent random properties and lazy model construction. (Interestingly, memoization has a semantic effect in probabilistic languages.) If we view the semantics of the underlying deterministic language as a map from programs to executions of the program, the semantics of the probabilistic language will be a map from programs to distributions over executions. When the program halts
Shilling attacks against recommender systems: a comprehensive survey
(2012)

Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.
Modeling people flow: simulation analysis of international-departure passenger flows in an airport terminal
(2003)

An entire airport terminal building is simulated to examine passenger flows, especially international departures. First, times needed for passengers to be processed in the terminal building are examined. It is found that the waiting time for check-in accounts for more than 80 percent of the total waiting time of passengers spent in the airport. A special-purpose data-generator is designed and developed to create experimental data for executing a simulation. It is found that the possible number of passengers missing their flights could be drastically reduced by adding supporting staff to and by making use of first-and business-class check-in counters for processing economy-and group-class passengers.
Enhancement of MRI human thigh muscle segmentation by template-based framework
(2014)

Image segmentation of anatomic structures is often an essential step in medical image analysis. A variety of segmentation methods have been proposed, but none provides automatic segmentation of the thigh. In magnetic resonance images of the thigh, the segmentation is complicated by factors, such as artifacts (e.g. intensity inhomogeneity and echo) and inconsistency of soft and hard tissue compositions, especially in muscle from older people, where accumulation of intermuscular fat is greater than in young muscles. In this paper, the combination framework that leads to a segmentation enhancement method for region of interest segmentation are demonstrated. Appropriate methods of image pre-processing, thresholding, manual interaction of muscle border, template conversion and deformable contours in combination with image filters are applied. Prior geometrical information in an initial template image is used to automatically derive the muscle outlines by application of snake active contours, in serial images within a single MRI dataset. Our approach has an average segmented output accuracy of 93.34% by Jaccard Similarity Index, and reduced the processing time by 97.73% per image compared to manual segmentation.
Age and gender estimation using deep residual learning network
(2018)

In this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database [4]. However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.
Misinformation in Online Social Networks: Detect Them All with a Limited Budget
(2016)

Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors.
 In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its #P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.
The pains and pleasures of parenting: when, why, and how is parenthood associated with more or less well-being?
(2014)

The relationship between parenthood and well-being has become a hot topic among scholars, media, and general public alike. The research, however, has been mixed-some studies indicate that parents are happier than nonparents, whereas others suggest the reverse. We suggest that the question of whether parents are more or less happy than their childless peers is not the most meaningful one. To reconcile the conflicting literature and expand understanding of the emotional experience of parenthood, we present a model of parents' well-being that describes why and how parents experience more or less happiness than nonparents (i.e., mediators of the link between parenthood and well-being). We then apply this model to explain when parents are more likely to experience more or less happiness (i.e., moderators of parents' well-being, such as parent age or child temperament). Supporting our model, we review 3 primary methodological approaches: studies comparing parents and nonparents, studies examining changes in well-being across the transition to parenthood, and studies comparing parents' experiences while with their children to their other daily activities. Our review suggests that the relationship between parenthood and well-being is highly complex. We propose that parents are unhappy to the extent that they encounter relatively greater negative emotions, magnified financial problems, more sleep disturbance, and troubled marriages. By contrast, when parents experience greater meaning in life, satisfaction of their basic needs, greater positive emotions, and enhanced social roles, they are met with happiness and joy.
On Semantic Algebra: A Denotational Mathematics for Natural Language Comprehension and Cognitive Computing
(2013)

Semantics is composed meaning of language expressions and perceptions at the levels of words, phrase, sentence, paragraph, and essay. The basic unit of formal semantics is a concept as a formal model of words in a natural language. Cognitive linguistics focuses on cognitive semantics and its interaction with underlying syntactic structures. A denotational mathematics known as semantic algebra is presented for rigorous semantics manipulations in cognitive linguistics. Semantic algebra deals with formal semantics of linguistics by a general mathematical model of semantics and a set of algebraic operators. On the basis of semantic algebra, language semantics may not only be deductively analyzed based on syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up at different levels of language units. Semantic algebra enables a wide range of applications in cognitive informatics, cognitive linguistics, computational linguistics, semantic computing, cognitive computing, machine learning, computing with words, as well as natural language analysis, synthesis, and comprehension.
Cross-Lingual Propagation for Deep Sentiment Analysis
(2018)

Across the globe, people are voicing their opinion in social media and various other online fora. Given such data, modern deep learning-based sentiment analysis methods excel at determining the sentiment polarity of what is being said about companies, products, etc. Unfortunately, such deep methods require significant training data, while for many languages, resources and training data are scarce. In this work, we present a cross-lingual propagation algorithm that yields sentiment embedding vectors for numerous languages. We then rely on a dual-channel convolutional neural architecture to incorporate them into the network. This allows us to achieve gains in deep sentiment analysis across a range of languages and domains.
The Ring of Gyges : Using Smart Contracts for Crime
(2015)

Thanks to their anonymity (pseudonymity) and lack of trusted intermediaries, cryptocurrencies such as Bitcoin have created or stimulated growth in many businesses and communities. Unfortunately, some are criminal, e.g., money laundering, marketplaces for illicit goods, and ransomware. Next-generation cryptocurrencies such as Ethereum will include rich scripting languages in support of smart contracts, programs that autonomously intermediate transactions. We illuminate the extent to which these new cryptocurrencies, by enabling criminal activities to be conducted anonymously and with minimal trust assumptions, may fuel new criminal ecosystems. Specifically, we show how what we call criminal smart contracts (CSCs) can facilitate leakage of confidential information, theft of cryptographic keys, and various realworld crimes (murder, arson, terrorism). We show significantly that CSCs for leakage of secrets are efficiently realizable in existing scripting languages such as that in Ethereum. We show that CSCs for key theft can be achieved using cryptographic primitives, such as Succinct Non-interactive ARguments of Knowledge (SNARKs), that are already expressible in these languages and for which efficient supporting language extensions are anticipated. We demonstrate similarly that authenticated data feeds, another anticipated feature of smart contract systems, can facilitate CSCs for real-world crimes. Our results illuminate the scope of possible abuses in nextgeneration cryptocurrencies. They highlight the urgency of creating policy and technical safeguards and thereby realizing the great promise of smart contracts for beneficial goals.
Opening the Black Box of Deep Neural Networks via Information
(2017)

Despite their great success, there is still no comprehensive theoretical understanding of learning with Deep Neural Networks (DNNs) or their inner organization. Previous work [Tishby and Zaslavsky (2015)] proposed to analyze DNNs in the Information Plane; i.e., the plane of the Mutual Information values that each layer preserves on the input and output variables. They suggested that the goal of the network is to optimize the Information Bottleneck (IB) tradeoff between compression and prediction, successively, for each layer. In this work we follow up on this idea and demonstrate the effectiveness of the InformationPlane visualization of DNNs. Our main results are: (i) most of the training epochs in standard DL are spent on compression of the input to efficient representation and not on fitting the training labels. (ii) The representation compression phase begins when the training errors becomes small and the Stochastic Gradient Decent (SGD) epochs change from a fast drift to smaller training error into a stochastic relaxation, or random diffusion, constrained by the training error value. (iii) The converged layers lie on or very close to the Information Bottleneck (IB) theoretical bound, and the maps from the input to any hidden layer and from this hidden layer to the output satisfy the IB self-consistent equations. This generalization through noise mechanism is unique to Deep Neural Networks and absent in one layer networks. (iv) The training time is dramatically reduced when adding more hidden layers. Thus the main advantage of the hidden layers is computational. This can be explained by the reduced relaxation time, as this it scales super-linearly (exponentially for simple diffusion) with the information compression from the previous layer. (v) As we expect critical slowing down of the stochastic relaxation near phase transitions on the IB curve, we expect the hidden layers to converge to such critical points.1
A comparative evaluation of procedural level generators in the Mario AI framework
(2014)

Evaluation is an open problem in procedural content generation research. The field is now in a state where there is a glut of content generators, each serving different purposes and using a variety of techniques. It is difficult to understand, quantitatively or qualitatively, what makes one generator different from another in terms of its output. To remedy this, we have conducted a large-scale comparative evaluation of level generators for the Mario AI Benchmark, a research-friendly clone of the classic platform game Super Mario Bros. In all, we compare the output of seven different level generators from the literature, based on different algorithmic methods, plus the levels from the original Super Mario Bros game. To compare them, we have defined six expressivity metrics, of which two are novel contributions in this paper. These metrics are shown to provide interestingly different characterizations of the level generators. The results presented in this paper, and the accompanying source code, is meant to become a benchmark against which to test new level generators and expressivity metrics.